Systematic design of a Maturity Model for the Development of New

Transkrypt

Gökhan Akkasoglu*
Albert Weckenmann**
Systematic design of a Maturity Model
for the Development of New Forming Processes
Introduction
The development of new forming processes aims primarily for increasing process robustness and shortening process chain. The latter reduces also process time and increases cost-effectiveness. Sheet metal forming processes are in the focus of current developments, since they enable
the increase of workpiece complexity by functional integration and meet
the aspects of lightweight construction. Within the framework of the
Transregional Collaborative Research Centre 73 different forming processes are analyzed, designed and optimized to unite the advantages and
design possibilities of sheet and bulk metal forming to the novel SheetBulk Metal Forming (SBMF) using semifinished sheet workpieces. The
SBMF is defined as “the plastic changing of the shape of a plain semifinished product not changing the specimen integrity. During the forming
process both two- and three-axial strain- and stress-conditions as well as
changes in shape occur simultaneously or shortly after each other” [Merklein, 2010].
The design and analysis of the SBMF is complex due to low previous
knowledge and distinct creative phases, which complicates decisionmaking due to an intransparent development status. Especially in early
development stages a low level of knowledge exists and the number of
recognizable and thus quantitatively assessable characteristics of the
manufacturing process is small [Reithmeier, 2011; Giapoulis, 2000] (Figure
1, left). Paradoxically, in these early stages the number of influenceable
features is to be classified as high. Towards the end of development the
number of recognizable features raises, but the number of influenceable
Dipl.-Ing., Chair Quality Management and Manufacturing Metrology, University Erlanen-Nuremberg, [email protected]
** Prof. Dr.-Ing. Prof. h.c. Dr.-Ing. E.h. Dr. h.c. mult., Chair Quality Management and
Manufacturing Metrology, University Erlanen-Nuremberg, [email protected]
*
192
Gökhan Akkasoglu, Albert Weckenmann
parameters decreases together with the change effort. That relates to the
increasing modification effort according to the rule-of-ten, which indicates
that the (financial) effort for changes is increasing tenfold with each development stage (Figure 1, right) [Ross, 1999]. Exemplary it is very costintensive to make major modifications on a forming tool which is already
built up, whereas it is easy to perform such a change during tool design in
a CAD-Model.
Recognizable
features
(Financial) change effort
Information
uncertainty
Number of product features
Information uncertainty
Figure 1. Uncertainty in development phases (left) and Rule-of-Ten (right)
1000,-
100,1,-
Development phases
10,Development phases
Source: [Reithmeier, 2011; Giapoulis, 2000; Ross, 1999].
An early characterization of the maturity before serial production
enables in accordance to the rule-of-ten cost-effective decisions and
modifications with reduced effort. A maturity represents the current
development status of a considered process, product or system to a specific time and can be assessed by comparison with relevant indicators
and their staged requirements within maturity levels [Weckenmann,
2011; Ahlemann, 2005]. Maturity models summarize these indicators
and staged requirements. Thus maturity models provide best-practices
for a subject matter to be evaluated by comparison. With determining
the current status of the considered processes, products or systems specific improvement possibilities can be detected. In addition maturity
models enable the capturing of lessons learned by adjusting the up to
now documented requirements. Typically maturity models consist of
four to six maturity levels.
Currently existing maturity models like CMMI [SEI 2010] or ISO
15504 [ISO 20003] have primarily a strategic focus and are inappropriate
for the evaluation of new forming processes due to their focus on software
development processes [Berg 2001]. There exist several other maturity
models for the evaluation of forming processes such as the Manufacturing
Readiness Level of the United States Department of Defense [DoD 2009],
the Process Survey Tool for Manufacturing Process Management of the
European Foundation for Quality Management [EFQM 2004] or the Ma-
Systematic design of a Maturity Model for the Development of New… 193
turity Level Assurance for new Parts of the German Association of the
Automotive Industry [VDA 2007]. But these maturity models do not consider either typical phases of simulation and experimentation in the development of the SBMF or manufacturing relevant aspects, why they do
not provide an adequate basis for the evaluation and assessment of the
development status of the SBMF to be developed. They have a reduced
relevance to the demands of an assessment for new forming processes.
Thus, provided improvement measures on the basis of the determined
maturity have low adaptability to the specific concern. Hence, a maturity
model for assessing new forming processes has to be designed systematically for the novel SBMF to enhance the adequacy of derived improvement actions and also their acceptance within organizations.
1. Systematic design of a Maturity Model for Sheet-Bulk Metal
Forming
A customized maturity model creates the possibility for adequate assessments and identifies appropriate improvement possibilities. Approaches for the development of maturity models are discussed in different
literature [Bruin, 2005; Becker, 2009], which have in common, that they do
not provide methodic support in designing maturity models. Although
creating a maturity model with methodical support would enhance the
efficiency and usability of the procedure.
For the methodic design of a maturity model initially a reference
model of the considered object is to be created, which can comprise its
structure or the elementary activities of a general process for a specific aim
(Figure 2). A reference model summarizes the considered object in a generalized way and defines the focus of the investigation. On the basis of the
reference model relevant maturity indicators are to be deduced by brainstorming and categorized to superordinate indicators, for example using
the quality management technique of affinity diagrams. The selected indicators have to be weighted with the help of a pairwise comparison. The so
called Maturity Level Matrix captures the defined indicators and their
weightings as well as the hierarchically staged requirements on the indicators. The requirements are assigned to maturity levels which in turn are
related to certain percentage intervals. The highest maturity value of 100%
represents the best-practice for the specific indicator. The maturity can be
determined by evaluating the indicators in percent with comparison of the
current status of a considered object and the fulfillment of the set require-
194
ments on the specific indicators with a subsequent calculation of the
weighted arithmetic mean.
Figure 2. Systematic approach for the design of a maturity model
Reference Model
Sustainable
Maturity indicators
Comprehensive
Level 4
Level 3
Appropriate
Ad hoc/
unstructured
Indicator weighting
Indi
Indi
…
Indn
…
...
…
...
…
Level 2
Wt.’s
Indn
…
Level 1
0% - 15%
15% - 50%
50% - 85%
85% - 100%
Maturity M
Maturity Level Matrix
Indi
wi
Maturity Levels
…
… Staged
...
...
...requirements
...
...
...
Evaluation
n
1
M(t) = n
wi ⋅ Ind i ( t )
wi i =1
∑
∑
i =1
Source: Own work.
The methodic procedure for the design of maturity models has been
adopted to create an assessment basis for the development status of the
novel sheet-bulk metal forming. Maturity relevant indicators are derived on
the basis of a phase model that classifies the elementary development steps.
Afterwards the weighted indicators are assigned to specific phases. For maturity determination all indicators assigned to the current development
phase as well as those in the previous phases are to be used.
The development of new forming processes takes place at the level of
digital models and experimental investigations [Brenner, 2010a], in order to
estimate the structure and behavior of cost-intensive components previously and to validate and improve them afterwards. The phase model for
the analysis and design of forming processes categorizes these significant
development periods and gathers the basic activities of the respective
phases, whereby a generalized procedure model is provided (Figure 3). In
addition, preparatory phases such as “system analysis” and “system design” are considered, which can influence significantly the fulfillment of
aims in simulative and experimental phases. Milestones (Mi) and Quality
Gates (Qi) at the end of each phase indicate points of time to evaluate the
maturity. In contrast to Quality Gates, Milestones can be passed without
meeting all requirements. Still, determined improvement measures have to
be applied until the next Milestone.
2. Maturity model for new forming processes
The indicators for the assessment of the development status are derived on the basis of specific activities of the phase model for new forming
processes (Figure 3). Figure 4 illustrates the elicited indicators with their
sub-indicators and their weightings determined by a pair-wise comparison.
The usefulness of defined aims and requirements is captured with the
indicator of the same name. It is also considered to what extent the state of
the art and market developments were implicated. Furthermore it is asked
about the implementation of a development timeline and a feasibility
study. The maturity indicator of “System analysis and design” assesses the
extent of the system definition, as well as the relevance and significance of
the acquired system parameters. The indicators “Modeling” captures the
model reduction (idealization degree of reality), the model equations used
and accuracy of the model geometry. The “Model verification” deals with
the extent of the discretization and numerical errors [Oberkampf, 2007]. The
indicator “Simulation” asks for the type of simulation used and the plausibility of the simulation results. The indicators on “Concept and design”,
“Experiments”, “Cause and effect relationships and interactions”, “Evaluation and validation” and “Optimization” have a generic nature and are
both used in the simulation phase as well as in the experimental phase to
assess the development status. The experimental environment and the extent of concept analysis as well as the inclusion of experimental data uncertainties, such as process variations are surveyed. Furthermore, the significance and the type of the determined causal relationships (e.g. analytic or
empiric) are assessed, so that the acquired knowledge on the new forming
process is appraised. Moreover, the designed and implemented system
elements are evaluated in terms of their functional performance, reliability
and robustness. The type of criteria (e.g. quantitative or qualitative) used
for evaluating the forming process provides information about the decision
Possible
influencing and
target quantities
as well as cause
and effect
relationships
gathered (2.2; 8.1).
Concepts of
forming tools,
machine components and workpieces designed
(1.2; 6.4).
Measurement
Causal relations
requirements and determined and
tasks defined (1.2). transferred to the
Model (3.2).
State of the art
and market
development of
forming tools,
machine
components and
workpieces
analyzed (1.1).
Source: Own work.
Manufacturing
and quality
objectives defined
(1.2).
Timeline of
development
established (1.3).
Simulation
D
Feasibility studies (Existing)
Model geometry
performed (1.4).
Measurement
designed (3.3).
systems evaluated
(12.1; 12.2).
M4
E
Virtual
optimization
Simulation
validated (5).
Lubrication
purchased and
evaluated (9.3; 9.4;
11.1; 11.2).
Forming tools and
machine components as well
as measurement
system integrated
(9.3; 9.4; 12.3).
H
Experimental
validation
Forming tools,
machine
components and
workpieces
evaluated (9).
Measurement
system designed
and evaluated
(12.1; 12.2).
Selected concepts Measurement
of forming tools and system validated
machine com(12.1; 12.2).
ponents produced
and launched
(9.3; 9.4).
I
Experimental
optimization
Q2
Parameter of
measurement
system optimized
(10).
Cause and effect
relationships of
process
parameters
dimensioned robust
(10).
Parameter of the
forming machine
components
dimensioned
optimally (10).
Tool-lubricantworkpiece-system
parameterized
optimally (10).
Manufacturing
process evaluated
and improvement
possibilities
identified (9).
M7
Cause and effect
relationships and
interactions
analyzed and
validated (8).
Experimental
concept analysis
performed on the
whole system
(7; 6.1; 6.2).
M6
Selected concepts Lubrication
Selected concepts Simulation model
of formed
selected (9.1; 9.2). of forming tools and validated und
workpieces
machine comcalibrated (9.2).
designed (6.4).
ponents
purchased
(9.1; 9.2; 11.1;
11.2; 9.3; 9.4).
Cause and effect Process
relationships and parameterized
interactions
optimally (10.1).
identified (8.1).
Simulation based
concept analysis
of forming tools,
machine
components and
workpieces
performed
(6.1; 6.2; 7.1).
Selected concepts
of forming tools and
machine
components
designed (6.4).
Concepts of
Make-or-Buy
formed workpieces decision made
revised, evaluated (9.1; 9.2).
and selected (6.3).
Concepts of
forming tools and
machine components revised,
evaluated and
selected (6.3).
G
Experimental
preparation
Aims and
requirements on
experiments
defined and
approach specified
(1.2; 1.3).
Q1
System design 2
F
Additional model
experiments
performed
(7; 9.1; 9.2; 9.3)
M5
Model verified (4). Cause and effect
relationships and
interactions
characterized (8.2).
M3
Characteristic
Approach for
values determined simulation tests
(7.1; 7.2; 12.2),
defined (7.1).
model parameterized (3.1).
Effort and
feasibility of
simulations and
experiments
compared (1.4).
Aims and
requirements on
simulation models
defined (1.2).
Modeling
C
System structure
and procedure
model created
(2.1).
M2
Current state of
forming tools ,
machine
components and
workpieces
analyzed (1.1).
B
System design 1
M1
System analysis
A
196
Figure 3. Phase model for the development of new forming processes
Figure 4. Maturity indicators and sub-indicators for the development of new
forming processes
1 Aims and requirements (11%)
1.1State of the art and market
development (2.75%)
1.2 Aims and requirements (4.4%)
1.3 Timeline (2.75%)
1.4 Feasibility studies (1.1%)
6 Concept and design (11%)
6.1 Investigation surroundings
f or concepts (2.2%)
6.2 Concept analysis (5.5%)
6.3 Concept evaluation and selection (1.1%)
6.4 Design (2.2%)
2 System analysis and design (7%)
2.1 System limitation and modeling (2.4%)
2.2 System parameters (4.6%)
3 Modeling (5%)
3.1 Model reduction / Idealization (2.5%)
3.2 Causal relations of parameters (1.5%)
3.3 Model geometry (1%)
4 Model verification (3%)
4.1 Approach (1.2%)
4.2 Discretization error (0.9%)
4.3 Numerical error (0.6%)
4.4 Benchmarking (0.3%)
5 Simulation (4%)
5.1 Type of simulation (0.8%)
5.2 Results (3.2%)
10 Optimization (7%)
10.1 Target value optimization (5.3%)
10.2 Ef fectiveness of optimization (1.7%)
11 Supplier management (2%)
11.1 Requirements specif ication (1.3%)
11.2 Supplier monitoring (0.7%)
7 Experiments (7%)
7.1 Approach (4.9%)
7.2 Data uncertainty (2.1%)
12 Measurement system (7%)
12.1 Analysis of measurement
system (2.8%)
12.2 Measurement conditions (2.1%)
12.3 Process integration (2.1%)
8 Cause and effect relationships
and interactions (11%)
8.1 Identif ication of causal relations (5.5%)
8.2 Characterization of causal relations
(5.5%)
13 Employee (7%)
13.1 Further education, motivation (3.5%)
13.2 Communication (3.5%)
9 Evaluation and validation (11%)
9.1 State of the art, requirements (1.7%)
9.2 Evaluation criteria and decision (2.1%)
9.3 Environmental conditions (1.7%)
9.4 Function f ulf illment, reliability and
robustness (5.5%)
14 Information management (7%)
14.1 Inf ormation system (3.5%)
14.2 Inf ormation content (3.5%)
Source: Own work.
reliability. The time-critical collaboration with suppliers is surveyed by validating the requirements specification and the supplier monitoring. The
measurement system is evaluated in terms of degree of analysis (taking
measurement uncertainty into account), the consideration of surrounding
conditions and the degree of process integration. The cross-phase factor of
“Employee”, who is identified as the main pillar of any development project, is considered with regard to education and training, motivation and
communication. The handling of the large amounts of data and information
acquired during the development of a forming process is evaluated by the
further cross-phase indicator “Information management” in terms of used
information systems and the information content stored therein. These indicators reflect the capability of the development for a high maturity.
Figure 5. Excerpt of the Maturity-Level-Matrix
Sub-indicator
"6 Concept and
design"
Maturity levels
Wt.
Level 1
Level 2
Level 3
Level 4
(0% - 15%)
(15% - 50%)
(50% - 85%)
(85% - 100%)
6.1 Investigation
surroundings for
concepts
2.2%
Concept investigations
are performed as a
sketch.
Simulation based
concept investigations.
Concepts with real model
experiments investigated
under laboratory
conditions.
Concept investigations
are performed within a
production environment
with comparable
influencing and noise
quantities .
6.2 Concept analysis
5.5%
No concept analysis and
selection.
Concepts are defined
unstructured and
designed with low
variation of parameters.
Significant parameters of
concepts are varied
(according to the
requirements) sufficiently.
Concept analysis is
performed according to a
reproducible approach by
extensive and
appropriate variation of
significant parameters.
Source: Own work.
198
The staged requirements on each sub-indicator are assigned to four
maturity levels within a Maturity Level Matrix (Figure 5). The percentage
intervals of the maturity levels comprise level 1 covering 0% to 15%, level 2
covering 15% to 50%, level 3 covering 50% to 85% and level 4 covering 85%
to 100%. The complete Maturity Level Matrix with all indicators is called
maturity model.
3. Exemplary appliance of the maturity model.
The validation of the established maturity model is carried out by an
instructed self-assessment within the development of the Sheet-Bulk Metal
Forming. The current development time is localized within the phase of
“Experimental preparation”. All assigned indicators up to that phase are
used to assess the maturity. The assessment was performed independently
by experts for deep drawing processes, forming machines and measurement systems. The maturity is determined in dependence of the phases and
the system elements. By these two perspectives on the maturity improvement opportunities can be identified specifically. The maturities are calculated using a weighted arithmetic mean [Brenner, 2010b] and are shown in
A maturity value of 100% corresponds to the full compliance of the
current development status with the requirements for this phase. Similarly,
the weighted maturity deviations are calculated and prioritized [Brenner,
2010b] to demonstrate the aim-oriented improvement possibilities. Specific
acronyms were used to indicate, to which field the evaluated generic maturity sub-indicators refer. Based on the determined phase-dependent maturities distinctive improvement opportunities result in the development
stage of “Virtual optimization”. Simulative optimization enables concept
studies with many variations in the run-up to real and cost-intensive experiments and provides a wide base of information and knowledge about
the process behavior. This knowledge can be used early in the design of
forming tools, for example to avoid costly changes at the already produced
tool. For the current phase of “Experimental preparation” the systematic
functional evaluation of the purchased tool for the deep drawing process
(indicator “9.4/TZ-P-WZ-B”) according to the defined criteria of the system
model and the elimination of the determined noise quantities for increasing
the robustness can be recommended to optimize the development status.
This improvement possibility can be seen also in the maturity perspective
related to the defined system elements. In addition, this perspective shows
improvement opportunities in the selection of the “Lubrication”. The evalu-
Source: Own work.
95 %
92 %
90 %
Workpiece
Tool, workpiece and
process meas.
technology
Forming machine
TZ
UM
WS
WZ
275
303
275
413
Deep Drawing
Forming Machine
Workpiece
Tool
110
98
7.1 / UM
42
91
6.2 / UM
9.4 / UM-H
12.3 / UM-P-MT
9.3 / WZ-MT
9.4 / WZ-MT
44
1.2 / UM-WS
55
49
6.2 / UM-WS
7.1 / UM-WS
55
9.2 / UM-S
9.3 / UM-S
83
138
9.4 / TZ-P-WZ-H
9.4 / UM-S
138
83
131
138
9.4 / UM-WZ
9.4 / TZ-P-WZ-B
8.2 / UM-Sim
10.1 / TZ-P-Sim
8.1 / UM
Weighted maturity deviation
Measurement Technique
Process
Lubrication
Simulation
59 %
Lubrication
MT
P
S
Sim
84 %
Tool
Used acronyms
B Purchase
H Design
KW Characteristic values
MS Measuring System
90 %
Maturity
Process design
System element
Maturity of system elements
Experimental
preparation
System design 2
Virtual Optimization
Simulation
Modeling
System design 1
System analysis
Phase
Maturity
80 %
95 %
85 %
93 %
92 %
94 %
87 %
55
55
53
6.4 / TZ-P-WZ
12.3 / UM-P-MT
303
275
9.4 / WZ-MT
9.4 / UM-H
9.4 / TZ-P-WZ-B
55
9.2 / UM-S
79
6.3 / TZ-P-WZ
10.1 / UM-Sim
131
83
8.2 / UM-Sim
10.1 / TZ-P-Sim
110
98
7.1 / UM
42
74
6.2 / UM
7.2 / UM-KW
7.1 / UM-KW
413
219
138
75
46
2.2 / UM
3.2 / TZ-P
48
55
105
2.1 / WZ-MT
8.1 / UM
1.3 / TZ-P
13.2
13.1
Weighted maturity deviation
Maturity of phases
Figure 6. Maturity evaluation subject to phases and system elements
200
ation of the lubricant should ideally be based on defined, quantitative
and multi-dimensional characteristics within a production environment
(with comparable influencing and noise quantities), where the functional performance is examined over a defined period.
Conclusion
A quality-oriented development of new forming processes requires
determination and managing of the development status based on reproducible evaluations to identify improvement opportunities in early development phases for enabling necessary changes at low costs. For the assessment of the development status and for the identification of improvement
possibilities a maturity model for new forming processes has been created.
The indicators are deduced on the basis of a combined reference model
(consisting of a system and a phase model) and extended to a maturity
model with specific maturity levels. The maturity assessment was carried
out independently from one another by members of the development team
for the Sheet-Bulk Metal Forming. The quantified development status gives
information about improvement possibilities to achieve the set aims.
Literature
1. Merklein M. et al. (2010), Manufacturing of Complex Functional Components with variants by using a new Metal Forming Process - Sheet-Bulk Metal Forming, „Intl. Journal of Material Forming” no. 1, s. 347-350.
2. Reithmeier E., Weckenmann A., Behrens B.-A. et al. (2011), Qualitätsorientierte Entwicklung der Blechmassivumformung, [w:] Tagungsband
zum 1. Erlanger Workshop Blechmassivumformung 2011, Merklein M.
(red.), Meisenbach, Bamberg, s. 97-118.
3. Giapoulis A. (2000), Einsatz von Methoden zur Produktentwicklung in der
industriellen Praxis, [w:] VDI Berichte 1558, VDI (red.), Düsseldorf, s. 1-9.
4. Ross J. E., Perry S. (1999), Total quality management: text, cases, and readings, St. Lucie Press, Florida.
5. Weckenmann A., Akkasoglu G. (2011), Maturity Method for the Development of Metal Forming Processes considering Fuzzy Input Parameters,
[w:] 6th Intl. Conf. on Design and Production of Machines and Dies/Molds,
Akkök M. et al. (red.), Atilim University, Ankara, s. 9-15.
6. Ahlemann F. et al. (2005), Kompetenz- und Reifegradmodelle für das Projektmanagement. Grundlagen, Vergleich und Einsatz, [w:] ISPRI-Work Report, Ahlemann F. et al. (red.), University of Osnabrück.
7. SEI (2010), CMMI for Development v1.3., Carnegie Mellon University,
Pittsburgh.
8. ISO 15504-2, Information technology - Process assessment – Part 2: Performing an assessment, Geneva, 2003.
9. Berg P. et al. (2001), Assessment of quality and maturity level of Research
and Development, Intl. Conf. on Management of Engineering and Technology PICMET, Portland.
10. Department of Defense (2009), Manufacturing Readiness Assessment
(MRA) Deskbook - Version 7.1., Department of Defense, Washington.
11. EFQM-Philips (2004), Process Survey Tool For Manufacturing Process
Management, EFQM, Brussels.
12. VDA (2007), Das gemeinsame Qualitätsmanagement in der Lieferkette: Produktentstehung; Reifegradabsicherung für Neuteile, VDA, Oberursel.
13. de Bruin T. et al. (2005), Understanding the Main Phases of Developing a
Maturity Assessment Model, 16th Australasian Conference on Information Systems, Sydney.
14. Becker J. et al. (2009), Developing Maturity Models for IT Management,
„Wirtschaftsinformatik” no. 3, s. 249-260.
15. Brenner P.-F., Weckenmann A., Akkasoglu G. (2010), Reifegradmethode
für neue Fertigungsverfahren, „Qualität und Zuverlässigkeit” no. 6,
s. 52-55.
16. Oberkampf W. L., Pilch M., Trucano T. G. (2007), Predictive Capability
Maturity Model for Computational Modeling and Simulation, Sandia National Laboratories, Washington.
17. Brenner P.-F., Akkasoglu G., Weckenmann A. (2010), Referenzmodellgestützte Reifegradabsicherung bei der Entwicklung neuer umformtechnischer
Verfahren, [w:] Unternehmerisches Qualitätsmanagement, Schmitt R.
(red.), Apprimus, Aachen, s. 123-144.
Acknowledgement
The authors thank the German Research Foundation (DFG) for funding this research within the framework of the Transregional Collaborative
Research Centre (Transregio) TR 73.
Summary
Development of new forming processes aims for overcoming the current
process limitations so that function extended components with a wider range of
application and lower manufacturing costs are producible. But the comprehensive and complex investigations of cause-effect relationships and interdepend-
202
encies in the development lead to an intransparent development status, whose
assessment is often based on undefined and not reproducible criteria. This can
result in wrong decisions with vain modifications that require subsequent
changes associated with an increased effort.
A valid characterization of the development status is needed to identify
improvement potentials in early development phases and thus to apply the
effective measures with reduced efforts. Maturity models provide essential indicators and assign their values to maturity levels, whereby a uniform assessment base is created to identify the development status reproducibly. Currently
there is neither a maturity model for the assessment of new forming processes
in development available nor a method-based procedure for the design of specifically needed maturity models.
Therefore, a systematic approach has been designed to be able to determine maturity-related indicators on the basis of a combined reference model.
The indicators are to be subsumed, weigthed and provided in a maturity-levelmatrix. This approach has been applied within the development of the novel
Sheet-Bulk Metal Forming which aims for uniting the advantages of sheet and
bulk metal forming processes. The designed maturity model for new forming
processes facilitates the assessment of the development status with referencebased indicators and provides more transparent results in comparison to subjective evaluations.
Keywords
Maturity model, Sheet-Bulk Metal Forming, development process, information
uncertainty

Systematic design of a Maturity Model for the Development of New

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